Transportation analytics is the application of computer technology, operational research, and statistics to solve transportation problems. Transportation analytics can include traffic flow analysis, which itself can include signalized intersection analysis. Generally, modern transportation analytics is carried out within a computerized information system and typically will involve extracting properties from large transportation related databases. Mathematics and statistics underpins the algorithms used in transportation analytics and comprises a large ongoing effort at many public and private institutions worldwide. Transportation analytics bridges the disciplines of computer science, statistics, and mathematics; however, data must still be acquired to feed the study and analysis of modern transportation systems. Effective transportation analytics can lead to improved road design, reduced traffic, greater fuel efficiency, and many other benefits.
There have been many efforts to gather practical data for transportation analytics because transportation analytics is the process of obtaining an optimal or realistic decision based on existing transportation data, including traffic data. Conventionally, transportation analytics relied on static sensors such as loop detectors cut into a road surface to detect vehicular traffic. However, more modern conventional sensors now include the use of information gathered from mobile sensors. These mobile sensors can be dedicated devices such as transponders affixed to vehicles to relate traffic information. Further, these mobile sensors can include sensors on non-dedicated devices such as relaying information from a global position satellite (GPS) mapping device that a person may have in their vehicle or bicycle. As electronics allow mobile devices to do more and become ever more portable, transportation analytics scientist can expect to gain access to a rapidly increasing volume of traffic data from mobile devices.
In an aspect, the proliferation of a huge numbers of modern mobile phones and similar devices is viewed as a prime opportunity to gather data for transportation analytics. Conventional sources of transportation data from mobile devices are based on a wide variety of location determination technologies, such as GPS, triangulation, multilateration, near-field communications, etc., that provide location data for a mobile device over time. These sources of data have provided the opportunity to study transportation phenomenon in real time or near real time, which can allow for the generation of traffic related data for numerous other systems, such as, traffic visualizations, accident reporting/response, road design, roadway signal control, routing systems, estimated travel time analysis, etc. It is easily foreseeable that as computers begin to operate vehicles on our roadways, the need for transportation analytics will be able to provide for optimizing travel parameters for fuel efficiency (such as minimizing braking between traffic signals or for heavy traffic), temporal efficiency (such as by avoiding traffic or poorly timed signals), etc.
Whereas conventional systems rely on technologies such as GPS, triangulation, multilateration, near-field communications, etc., the use of timed fingerprint location (TFL) technology can provide advantages over the conventional technologies. For example, GPS is well known to be energy intensive and to suffer from signal confusion in areas with interference between the satellite constellation and the GPS enabled device. Further, GPS is simply not available on many mobile devices, especially where the devices are cost sensitive. Near-field communications technologies suffer from similar challenges as faced by GPS technologies and additionally require the use of additional hardware, such as beacons or receiver/transponders that must be located near enough to the near-filed sensor to operate. Multilateration and triangulation technologies are computationally intensive, which can result in processing time issues and a corresponding level of energy consumption.
The above-described deficiencies of conventional mobile device location data sources for transportation analytics is merely intended to provide an overview of some of problems of current technology, and are not intended to be exhaustive. Other problems with the state of the art, and corresponding benefits of some of the various non-limiting embodiments described herein, may become further apparent upon review of the following detailed description.